As we all know, rich and accurate image features are essential for SR image reconstruction. Meanwhile, plenty of research works [1]}, [2]}, [3]} have pointed out that images may exhibit different characteristics at different scales and thus making full use of these features can further improve model performance. Inspired by the inception module [2]}, Li et al. [5]} proposed a multi-scale residual block (MSRB, Fig. REF ) for feature extraction. MSRB integrates different convolution kernels in a block to adaptively extract image features at different scales. After that, Li et al. [6]} further optimized the structure and proposed a more efficient multi-scale dense cross block (MDCB) for feature extraction. MDCB is essentially a dual-path dense network that can effectively detect local and multi-scale features.
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